{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T18:27:17Z","timestamp":1778264837029,"version":"3.51.4"},"reference-count":69,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T00:00:00Z","timestamp":1650585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"United Nations Economic and Social Commission for Asia and the Pacific","award":["10141267"],"award-info":[{"award-number":["10141267"]}]},{"name":"the Hong Kong Research Grants Council","award":["AoE\/E-603\/18"],"award-info":[{"award-number":["AoE\/E-603\/18"]}]},{"name":"the National Key R&amp;D Program of China","award":["2019YFC1510400"],"award-info":[{"award-number":["2019YFC1510400"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Urban resilience to natural disasters (e.g., flooding), in the context of climate change, has been becoming increasingly important for the sustainable development of cities. This paper presents a method to assess the urban resilience to flooding in terms of the recovery rate of different subdistricts in a city using all-weather synthetic aperture radar imagery (i.e., Sentinel-1A imagery). The factors that influence resilience, and their relative importance, are then determined through principal component analysis. Jakarta, a flood-prone city in Indonesia, is selected as a case study. The resilience of 42 subdistricts in Jakarta, with their gross domestic product data super-resolved using nighttime-light satellite images, was assessed. The association between resilience levels and influencing factors, such as topology, mixtures of religion, and points-of-interest density, were subsequently derived. Topographic factors, such as elevation (coefficient = 0.3784) and slope (coefficient = 0.1079), were found to have the strongest positive influence on flood recovery, whereas population density (coefficient = \u22120.1774) a negative effect. These findings provide evidence for policymakers to make more pertinent strategies to improve flood resilience, especially in subdistricts with lower resilience levels.<\/jats:p>","DOI":"10.3390\/rs14092010","type":"journal-article","created":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T00:45:21Z","timestamp":1650761121000},"page":"2010","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Assessment and Improvement of Urban Resilience to Flooding at a Subdistrict Level Using Multi-Source Geospatial Data: Jakarta as a Case Study"],"prefix":"10.3390","volume":"14","author":[{"given":"Hui","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China"},{"name":"Shenzhen Real Estate Assessment Center, Shenzhen 518040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqian","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Surveying and Geoinformatics, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingkai","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China"},{"name":"South China Academy of Natural Resources Science and Technology, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Gou","sequence":"additional","affiliation":[{"name":"Department of Surveying and Geoinformatics, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongrong","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanqing","family":"Qiu","sequence":"additional","affiliation":[{"name":"Guangdong Urban & Rural Planning and Design Institute, Guangzhou 510290, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueming","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China"},{"name":"South China Academy of Natural Resources Science and Technology, Guangzhou 510642, China"},{"name":"College of Tropical Crops, Hainan University, Haikou 570228, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5063-3522","authenticated-orcid":false,"given":"Bo","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China"},{"name":"Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,22]]},"reference":[{"key":"ref_1","unstructured":"Tomas, H., and Etienne, T. 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